# FMG

**Repository Path**: hhd9497/FMG

## Basic Information

- **Project Name**: FMG
- **Description**: KDD17_FMG
- **Primary Language**: Matlab
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-05-18
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# FMG
The code KDD17 paper "[Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks](http://www.cse.ust.hk/~hzhaoaf/data/kdd17-paper.pdf)" and Arvix preprint "Learning with Heterogeneous Side Information Fusion for Recommender Systems (https://arxiv.org/pdf/1801.02411.pdf)"

Readers are welcomed to fork this repository to reproduce the experiments and follow our work. Please kindly cite our paper

    @inproceedings{zhao2017meta,
    title={Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks},
    author={Zhao, Huan and Yao, Quanming and Li, Jianda and Song, Yangqiu and Lee, Dik Lun},
    booktitle={KDD},
    pages={635--644},
    year={2017}
    }
    
    @techreport{zhao2018learning,
    title={Learning with Heterogeneous Side Information Fusion for Recommender Systems},
    author={Zhao, Huan and Yao, Quanming and Song, Yangqiu and Kwok, James and Lee, Dik Lun},
    institution = {arXiv preprint arXiv:1801.02411},
    year={2018}
    }
    
We released related datasets: *yelp-200k*, *amazon-200k*, *yelp-50k* and *amazon-50k*. Any problems, you can create an issue. Note that the amazon dataset is provied by Prof. [Julian McAuley](http://cseweb.ucsd.edu/~jmcauley/), thus if you use this dataset for your paper, please cite the authors' paper as instructed in the website http://jmcauley.ucsd.edu/data/amazon/ 

## Instructions

For the sake of ease, a quick instruction is given for readers to reproduce the whole process on yelp-50k dataset. Note that the programs are testd on **Linux(CentOS release 6.9), Python 2.7 and Numpy 1.14.0 from Anaconda 4.3.6.**

### Prerequisites

1. Unzip the file **FMG_released_data.zip**, and create a directory "data" in this project directory.
2. Move yelp-50k and amazon-50k into the "data" directory, then iteratively create directories **"sim\_res/path\_count"** and **"mf\_features/path\_count"** in directory **"data/yelp-50k/exp_split/1/"**.
3. Create directory **"log"** in the project by "mkdir log".
4. Create directory **"fm\_res"** in the project by "mkdir fm\_res".

### Meta-graph Similarity Matrices Computation.
To generate the similarity matrices on yelp-50k dataset, run

	python 200k_commu_mat_computation.py yelp-50k all 1
The arguments are explained in the following:
	
	yelp-50k: specify the dataset.
	all: run for all pre-defined meta-graphs.
	1: run for the split dataset 1, i.e., exp_split/1
One dependent lib is bottleneck, you may install it with "**pip install bottleneck**".

### Meta-graph Latent Features Generation.
To generate the latent features by MF based on the simiarity matrices, run
    
    python mf_features_generator.py yelp-50k all 1

The arguments are the same as the above ones.

Note that, to improve the computation efficiency, some modules are implements with C and called in python(see *load_lib* method in mf.py). Thus to successfully run mf\_features\_generator.py, you need to compile two C source files. The following scripts are tested on CentOS, and readers may take as references.

	gcc -fPIC --shared setVal.c -o setVal.so
	gcc -fPIC --shared partXY.c -o partXY.so

After the compiling, you will get two files in the project directory "setVal.so" and "partXY.so".

### FMG
After obtain the latent features, then the readers can run FMG model as following:
    
    python run_exp.py config/yelp-50k.yaml -reg 0.5

One may read the comment in files in directory config for more information.

## Misc
If you have any questions about this project, **you can open issues**, thus it can help more people who are interested in this project.
I will reply to your issues as soon as possible.